MétaCan
Menu
Back to cohort
Record W4411046798 · doi:10.1016/j.trf.2025.05.036

Advancing eHMI for powered wheelchairs beyond safety and communication: a pilot study on enriching social interaction through a co-design approach

2025· article· en· W4411046798 on OpenAlexaff
Mohsen Rasoulivalajoozi, Morteza Farhoudi

Bibliographic record

VenueTransportation Research Part F Traffic Psychology and Behaviour · 2025
Typearticle
Languageen
FieldComputer Science
TopicPersona Design and Applications
Canadian institutionsOntario College of Art and DesignConcordia University
Fundersnot available
KeywordsEngineeringHuman factors and ergonomicsPoison controlOccupational safety and healthSuicide preventionInjury preventionTransport engineeringMedical emergencyMedicine

Abstract

fetched live from OpenAlex

Background Enhancing safety and communication while minimizing unwanted attention is key for wheelchair external Human-Machine Interfaces (eHMIs). This study aims to introduce an interface to enhance eHMIs for powered wheelchairs, improve external communication, and enhance positive social interactions in challenging urban situations. Methods A co-design approach was adopted, centering wheelchair users (WUs) in a two-step methodology. First, data were collected through a qualitative survey to define criteria, which informed themes for focus group discussions. These themes guided the ideation process. Eighteen participants, including WUs and experts in cognitive psychology, physiotherapy, and design, were involved. Concepts developed in ideation sessions were analyzed using the Analytic Hierarchy Process. A prototype was then developed to be assessed by both WUs and pedestrians through a structured questionnaire. Results According to the analysis, four themes were identified: I. Streamlined Information in Interaction , II. User-Centric Safety Feedback , III. Harmonious and Minimalist Interaction Design , and IV. Effortless Integration and Production . Regarding these themes, a table with design suggestions and implications was introduced. Ultimately, five interface concepts were proposed, with Concept 2, ‘WheelSafe Illumina’ (41.3%), and Concept 1, ‘WheelGlow Assist’ (28.1%) emerging as top priorities, both featuring a shell structure. Concept 2 was developed for prototyping. The feedback from the experiences of both WUs and pedestrians indicate that the proposed eHMI may enhance perceived communication and safety without drawing negative attention. Conclusion Integrating eHMI into a shell structure improves functional communication while also minimizing unwanted attention toward WUs—an often-overlooked issue in previous research that our co-design approach identified and effectively addressed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.149
GPT teacher head0.454
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Part F Traffic Psychology and BehaviourSame topicPersona Design and ApplicationsFrench-language works237,207